House Prices - Kaggle Prediction Competition
Winn Pham
2023-06-21
1. Getting prepared
First of all, I install and load the required packages:
# install.packages("tidyverse")
# install.packages("ggplot2")
# install.packages("plotly")
# install.packages("reshape2")
# install.packages("moments")
# install.packages("dplyr")
# install.packages("e1071")
# install.packages("glmnet")
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ✔ purrr 1.0.1
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library(plotly)##
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## layoutlibrary(reshape2)##
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## smithslibrary(moments)
library(dplyr)Then, I upload the training dataset and call it train_data:
train_data <- read_csv("train.csv")## Rows: 1460 Columns: 81
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (43): MSZoning, Street, Alley, LotShape, LandContour, Utilities, LotConf...
## dbl (38): Id, MSSubClass, LotFrontage, LotArea, OverallQual, OverallCond, Ye...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Afterwards, I explore the dataset and check for data completeness.
head(train_data)summary(train_data)## Id MSSubClass MSZoning LotFrontage
## Min. : 1.0 Min. : 20.0 Length:1460 Min. : 21.00
## 1st Qu.: 365.8 1st Qu.: 20.0 Class :character 1st Qu.: 59.00
## Median : 730.5 Median : 50.0 Mode :character Median : 69.00
## Mean : 730.5 Mean : 56.9 Mean : 70.05
## 3rd Qu.:1095.2 3rd Qu.: 70.0 3rd Qu.: 80.00
## Max. :1460.0 Max. :190.0 Max. :313.00
## NA's :259
## LotArea Street Alley LotShape
## Min. : 1300 Length:1460 Length:1460 Length:1460
## 1st Qu.: 7554 Class :character Class :character Class :character
## Median : 9478 Mode :character Mode :character Mode :character
## Mean : 10517
## 3rd Qu.: 11602
## Max. :215245
##
## LandContour Utilities LotConfig LandSlope
## Length:1460 Length:1460 Length:1460 Length:1460
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Neighborhood Condition1 Condition2 BldgType
## Length:1460 Length:1460 Length:1460 Length:1460
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## HouseStyle OverallQual OverallCond YearBuilt
## Length:1460 Min. : 1.000 Min. :1.000 Min. :1872
## Class :character 1st Qu.: 5.000 1st Qu.:5.000 1st Qu.:1954
## Mode :character Median : 6.000 Median :5.000 Median :1973
## Mean : 6.099 Mean :5.575 Mean :1971
## 3rd Qu.: 7.000 3rd Qu.:6.000 3rd Qu.:2000
## Max. :10.000 Max. :9.000 Max. :2010
##
## YearRemodAdd RoofStyle RoofMatl Exterior1st
## Min. :1950 Length:1460 Length:1460 Length:1460
## 1st Qu.:1967 Class :character Class :character Class :character
## Median :1994 Mode :character Mode :character Mode :character
## Mean :1985
## 3rd Qu.:2004
## Max. :2010
##
## Exterior2nd MasVnrType MasVnrArea ExterQual
## Length:1460 Length:1460 Min. : 0.0 Length:1460
## Class :character Class :character 1st Qu.: 0.0 Class :character
## Mode :character Mode :character Median : 0.0 Mode :character
## Mean : 103.7
## 3rd Qu.: 166.0
## Max. :1600.0
## NA's :8
## ExterCond Foundation BsmtQual BsmtCond
## Length:1460 Length:1460 Length:1460 Length:1460
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## BsmtExposure BsmtFinType1 BsmtFinSF1 BsmtFinType2
## Length:1460 Length:1460 Min. : 0.0 Length:1460
## Class :character Class :character 1st Qu.: 0.0 Class :character
## Mode :character Mode :character Median : 383.5 Mode :character
## Mean : 443.6
## 3rd Qu.: 712.2
## Max. :5644.0
##
## BsmtFinSF2 BsmtUnfSF TotalBsmtSF Heating
## Min. : 0.00 Min. : 0.0 Min. : 0.0 Length:1460
## 1st Qu.: 0.00 1st Qu.: 223.0 1st Qu.: 795.8 Class :character
## Median : 0.00 Median : 477.5 Median : 991.5 Mode :character
## Mean : 46.55 Mean : 567.2 Mean :1057.4
## 3rd Qu.: 0.00 3rd Qu.: 808.0 3rd Qu.:1298.2
## Max. :1474.00 Max. :2336.0 Max. :6110.0
##
## HeatingQC CentralAir Electrical 1stFlrSF
## Length:1460 Length:1460 Length:1460 Min. : 334
## Class :character Class :character Class :character 1st Qu.: 882
## Mode :character Mode :character Mode :character Median :1087
## Mean :1163
## 3rd Qu.:1391
## Max. :4692
##
## 2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath
## Min. : 0 Min. : 0.000 Min. : 334 Min. :0.0000
## 1st Qu.: 0 1st Qu.: 0.000 1st Qu.:1130 1st Qu.:0.0000
## Median : 0 Median : 0.000 Median :1464 Median :0.0000
## Mean : 347 Mean : 5.845 Mean :1515 Mean :0.4253
## 3rd Qu.: 728 3rd Qu.: 0.000 3rd Qu.:1777 3rd Qu.:1.0000
## Max. :2065 Max. :572.000 Max. :5642 Max. :3.0000
##
## BsmtHalfBath FullBath HalfBath BedroomAbvGr
## Min. :0.00000 Min. :0.000 Min. :0.0000 Min. :0.000
## 1st Qu.:0.00000 1st Qu.:1.000 1st Qu.:0.0000 1st Qu.:2.000
## Median :0.00000 Median :2.000 Median :0.0000 Median :3.000
## Mean :0.05753 Mean :1.565 Mean :0.3829 Mean :2.866
## 3rd Qu.:0.00000 3rd Qu.:2.000 3rd Qu.:1.0000 3rd Qu.:3.000
## Max. :2.00000 Max. :3.000 Max. :2.0000 Max. :8.000
##
## KitchenAbvGr KitchenQual TotRmsAbvGrd Functional
## Min. :0.000 Length:1460 Min. : 2.000 Length:1460
## 1st Qu.:1.000 Class :character 1st Qu.: 5.000 Class :character
## Median :1.000 Mode :character Median : 6.000 Mode :character
## Mean :1.047 Mean : 6.518
## 3rd Qu.:1.000 3rd Qu.: 7.000
## Max. :3.000 Max. :14.000
##
## Fireplaces FireplaceQu GarageType GarageYrBlt
## Min. :0.000 Length:1460 Length:1460 Min. :1900
## 1st Qu.:0.000 Class :character Class :character 1st Qu.:1961
## Median :1.000 Mode :character Mode :character Median :1980
## Mean :0.613 Mean :1979
## 3rd Qu.:1.000 3rd Qu.:2002
## Max. :3.000 Max. :2010
## NA's :81
## GarageFinish GarageCars GarageArea GarageQual
## Length:1460 Min. :0.000 Min. : 0.0 Length:1460
## Class :character 1st Qu.:1.000 1st Qu.: 334.5 Class :character
## Mode :character Median :2.000 Median : 480.0 Mode :character
## Mean :1.767 Mean : 473.0
## 3rd Qu.:2.000 3rd Qu.: 576.0
## Max. :4.000 Max. :1418.0
##
## GarageCond PavedDrive WoodDeckSF OpenPorchSF
## Length:1460 Length:1460 Min. : 0.00 Min. : 0.00
## Class :character Class :character 1st Qu.: 0.00 1st Qu.: 0.00
## Mode :character Mode :character Median : 0.00 Median : 25.00
## Mean : 94.24 Mean : 46.66
## 3rd Qu.:168.00 3rd Qu.: 68.00
## Max. :857.00 Max. :547.00
##
## EnclosedPorch 3SsnPorch ScreenPorch PoolArea
## Min. : 0.00 Min. : 0.00 Min. : 0.00 Min. : 0.000
## 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.000
## Median : 0.00 Median : 0.00 Median : 0.00 Median : 0.000
## Mean : 21.95 Mean : 3.41 Mean : 15.06 Mean : 2.759
## 3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.: 0.00 3rd Qu.: 0.000
## Max. :552.00 Max. :508.00 Max. :480.00 Max. :738.000
##
## PoolQC Fence MiscFeature MiscVal
## Length:1460 Length:1460 Length:1460 Min. : 0.00
## Class :character Class :character Class :character 1st Qu.: 0.00
## Mode :character Mode :character Mode :character Median : 0.00
## Mean : 43.49
## 3rd Qu.: 0.00
## Max. :15500.00
##
## MoSold YrSold SaleType SaleCondition
## Min. : 1.000 Min. :2006 Length:1460 Length:1460
## 1st Qu.: 5.000 1st Qu.:2007 Class :character Class :character
## Median : 6.000 Median :2008 Mode :character Mode :character
## Mean : 6.322 Mean :2008
## 3rd Qu.: 8.000 3rd Qu.:2009
## Max. :12.000 Max. :2010
##
## SalePrice
## Min. : 34900
## 1st Qu.:129975
## Median :163000
## Mean :180921
## 3rd Qu.:214000
## Max. :755000
## glimpse(train_data)## Rows: 1,460
## Columns: 81
## $ Id <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1…
## $ MSSubClass <dbl> 60, 20, 60, 70, 60, 50, 20, 60, 50, 190, 20, 60, 20, 20,…
## $ MSZoning <chr> "RL", "RL", "RL", "RL", "RL", "RL", "RL", "RL", "RM", "R…
## $ LotFrontage <dbl> 65, 80, 68, 60, 84, 85, 75, NA, 51, 50, 70, 85, NA, 91, …
## $ LotArea <dbl> 8450, 9600, 11250, 9550, 14260, 14115, 10084, 10382, 612…
## $ Street <chr> "Pave", "Pave", "Pave", "Pave", "Pave", "Pave", "Pave", …
## $ Alley <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ LotShape <chr> "Reg", "Reg", "IR1", "IR1", "IR1", "IR1", "Reg", "IR1", …
## $ LandContour <chr> "Lvl", "Lvl", "Lvl", "Lvl", "Lvl", "Lvl", "Lvl", "Lvl", …
## $ Utilities <chr> "AllPub", "AllPub", "AllPub", "AllPub", "AllPub", "AllPu…
## $ LotConfig <chr> "Inside", "FR2", "Inside", "Corner", "FR2", "Inside", "I…
## $ LandSlope <chr> "Gtl", "Gtl", "Gtl", "Gtl", "Gtl", "Gtl", "Gtl", "Gtl", …
## $ Neighborhood <chr> "CollgCr", "Veenker", "CollgCr", "Crawfor", "NoRidge", "…
## $ Condition1 <chr> "Norm", "Feedr", "Norm", "Norm", "Norm", "Norm", "Norm",…
## $ Condition2 <chr> "Norm", "Norm", "Norm", "Norm", "Norm", "Norm", "Norm", …
## $ BldgType <chr> "1Fam", "1Fam", "1Fam", "1Fam", "1Fam", "1Fam", "1Fam", …
## $ HouseStyle <chr> "2Story", "1Story", "2Story", "2Story", "2Story", "1.5Fi…
## $ OverallQual <dbl> 7, 6, 7, 7, 8, 5, 8, 7, 7, 5, 5, 9, 5, 7, 6, 7, 6, 4, 5,…
## $ OverallCond <dbl> 5, 8, 5, 5, 5, 5, 5, 6, 5, 6, 5, 5, 6, 5, 5, 8, 7, 5, 5,…
## $ YearBuilt <dbl> 2003, 1976, 2001, 1915, 2000, 1993, 2004, 1973, 1931, 19…
## $ YearRemodAdd <dbl> 2003, 1976, 2002, 1970, 2000, 1995, 2005, 1973, 1950, 19…
## $ RoofStyle <chr> "Gable", "Gable", "Gable", "Gable", "Gable", "Gable", "G…
## $ RoofMatl <chr> "CompShg", "CompShg", "CompShg", "CompShg", "CompShg", "…
## $ Exterior1st <chr> "VinylSd", "MetalSd", "VinylSd", "Wd Sdng", "VinylSd", "…
## $ Exterior2nd <chr> "VinylSd", "MetalSd", "VinylSd", "Wd Shng", "VinylSd", "…
## $ MasVnrType <chr> "BrkFace", "None", "BrkFace", "None", "BrkFace", "None",…
## $ MasVnrArea <dbl> 196, 0, 162, 0, 350, 0, 186, 240, 0, 0, 0, 286, 0, 306, …
## $ ExterQual <chr> "Gd", "TA", "Gd", "TA", "Gd", "TA", "Gd", "TA", "TA", "T…
## $ ExterCond <chr> "TA", "TA", "TA", "TA", "TA", "TA", "TA", "TA", "TA", "T…
## $ Foundation <chr> "PConc", "CBlock", "PConc", "BrkTil", "PConc", "Wood", "…
## $ BsmtQual <chr> "Gd", "Gd", "Gd", "TA", "Gd", "Gd", "Ex", "Gd", "TA", "T…
## $ BsmtCond <chr> "TA", "TA", "TA", "Gd", "TA", "TA", "TA", "TA", "TA", "T…
## $ BsmtExposure <chr> "No", "Gd", "Mn", "No", "Av", "No", "Av", "Mn", "No", "N…
## $ BsmtFinType1 <chr> "GLQ", "ALQ", "GLQ", "ALQ", "GLQ", "GLQ", "GLQ", "ALQ", …
## $ BsmtFinSF1 <dbl> 706, 978, 486, 216, 655, 732, 1369, 859, 0, 851, 906, 99…
## $ BsmtFinType2 <chr> "Unf", "Unf", "Unf", "Unf", "Unf", "Unf", "Unf", "BLQ", …
## $ BsmtFinSF2 <dbl> 0, 0, 0, 0, 0, 0, 0, 32, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ BsmtUnfSF <dbl> 150, 284, 434, 540, 490, 64, 317, 216, 952, 140, 134, 17…
## $ TotalBsmtSF <dbl> 856, 1262, 920, 756, 1145, 796, 1686, 1107, 952, 991, 10…
## $ Heating <chr> "GasA", "GasA", "GasA", "GasA", "GasA", "GasA", "GasA", …
## $ HeatingQC <chr> "Ex", "Ex", "Ex", "Gd", "Ex", "Ex", "Ex", "Ex", "Gd", "E…
## $ CentralAir <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "…
## $ Electrical <chr> "SBrkr", "SBrkr", "SBrkr", "SBrkr", "SBrkr", "SBrkr", "S…
## $ `1stFlrSF` <dbl> 856, 1262, 920, 961, 1145, 796, 1694, 1107, 1022, 1077, …
## $ `2ndFlrSF` <dbl> 854, 0, 866, 756, 1053, 566, 0, 983, 752, 0, 0, 1142, 0,…
## $ LowQualFinSF <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ GrLivArea <dbl> 1710, 1262, 1786, 1717, 2198, 1362, 1694, 2090, 1774, 10…
## $ BsmtFullBath <dbl> 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1,…
## $ BsmtHalfBath <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ FullBath <dbl> 2, 2, 2, 1, 2, 1, 2, 2, 2, 1, 1, 3, 1, 2, 1, 1, 1, 2, 1,…
## $ HalfBath <dbl> 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,…
## $ BedroomAbvGr <dbl> 3, 3, 3, 3, 4, 1, 3, 3, 2, 2, 3, 4, 2, 3, 2, 2, 2, 2, 3,…
## $ KitchenAbvGr <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1,…
## $ KitchenQual <chr> "Gd", "TA", "Gd", "Gd", "Gd", "TA", "Gd", "TA", "TA", "T…
## $ TotRmsAbvGrd <dbl> 8, 6, 6, 7, 9, 5, 7, 7, 8, 5, 5, 11, 4, 7, 5, 5, 5, 6, 6…
## $ Functional <chr> "Typ", "Typ", "Typ", "Typ", "Typ", "Typ", "Typ", "Typ", …
## $ Fireplaces <dbl> 0, 1, 1, 1, 1, 0, 1, 2, 2, 2, 0, 2, 0, 1, 1, 0, 1, 0, 0,…
## $ FireplaceQu <chr> NA, "TA", "TA", "Gd", "TA", NA, "Gd", "TA", "TA", "TA", …
## $ GarageType <chr> "Attchd", "Attchd", "Attchd", "Detchd", "Attchd", "Attch…
## $ GarageYrBlt <dbl> 2003, 1976, 2001, 1998, 2000, 1993, 2004, 1973, 1931, 19…
## $ GarageFinish <chr> "RFn", "RFn", "RFn", "Unf", "RFn", "Unf", "RFn", "RFn", …
## $ GarageCars <dbl> 2, 2, 2, 3, 3, 2, 2, 2, 2, 1, 1, 3, 1, 3, 1, 2, 2, 2, 2,…
## $ GarageArea <dbl> 548, 460, 608, 642, 836, 480, 636, 484, 468, 205, 384, 7…
## $ GarageQual <chr> "TA", "TA", "TA", "TA", "TA", "TA", "TA", "TA", "Fa", "G…
## $ GarageCond <chr> "TA", "TA", "TA", "TA", "TA", "TA", "TA", "TA", "TA", "T…
## $ PavedDrive <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "…
## $ WoodDeckSF <dbl> 0, 298, 0, 0, 192, 40, 255, 235, 90, 0, 0, 147, 140, 160…
## $ OpenPorchSF <dbl> 61, 0, 42, 35, 84, 30, 57, 204, 0, 4, 0, 21, 0, 33, 213,…
## $ EnclosedPorch <dbl> 0, 0, 0, 272, 0, 0, 0, 228, 205, 0, 0, 0, 0, 0, 176, 0, …
## $ `3SsnPorch` <dbl> 0, 0, 0, 0, 0, 320, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ ScreenPorch <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 176, 0, 0, 0, 0, 0, …
## $ PoolArea <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ PoolQC <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ Fence <chr> NA, NA, NA, NA, NA, "MnPrv", NA, NA, NA, NA, NA, NA, NA,…
## $ MiscFeature <chr> NA, NA, NA, NA, NA, "Shed", NA, "Shed", NA, NA, NA, NA, …
## $ MiscVal <dbl> 0, 0, 0, 0, 0, 700, 0, 350, 0, 0, 0, 0, 0, 0, 0, 0, 700,…
## $ MoSold <dbl> 2, 5, 9, 2, 12, 10, 8, 11, 4, 1, 2, 7, 9, 8, 5, 7, 3, 10…
## $ YrSold <dbl> 2008, 2007, 2008, 2006, 2008, 2009, 2007, 2009, 2008, 20…
## $ SaleType <chr> "WD", "WD", "WD", "WD", "WD", "WD", "WD", "WD", "WD", "W…
## $ SaleCondition <chr> "Normal", "Normal", "Normal", "Abnorml", "Normal", "Norm…
## $ SalePrice <dbl> 208500, 181500, 223500, 140000, 250000, 143000, 307000, …There are 1460 observations and 81 variables including 1 target variable as SalePrice and 80 explainable variables as different house features that may or may not relate the buying decision of a house. Besides, from the first few observations, I realized that there are some variables containing a lot of missing values in which I will investigate further in the next part of the study.
2. Data Exploration
2.1 Target variable
First of all, I started with the most important target variable, SalePrice. I evaluated it further by performing the statistic summary, visualize its distribution through the Histogram and check the skewness and kurtosis of the data distribution.
##Statistics summary of the Sale price variable
summary(train_data$SalePrice)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 34900 129975 163000 180921 214000 755000## create the Histogram to visualize the distribution of SalePrice
p <- ggplot(data= train_data) + aes(x=SalePrice) + geom_histogram()
fig <- ggplotly(p)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.fig##Skewness and Kurtosis of SalePrice distribution
Skew_SP <-skewness(train_data$SalePrice)
print(paste("Skewness: ",Skew_SP))## [1] "Skewness: 1.88094074603404"Kurt_SP <- kurtosis(train_data$SalePrice)
print(paste("Kurtosis: ", Kurt_SP))## [1] "Kurtosis: 9.50981201108944"From the graph, I can see that the SalePrice variable deviates from the normal distribution and is skewed to the right or has positive skewness.
Since skewness measure a degree of asymmetry of data distribution that deviates from the symmetrical normal distribution. The higher level of skewness can cause misleading result. So the skewness value of 1.9 shows that the data are extremely skewed and data transformation tools like log transformation might be needed to make the skewed data closer to a normal distribution.
Kurtosis measures the heaviness of a distribution’s tails relative to a normal distribution. The value of Kurtosis of 9.5, higher than 3, is called positive Kurtosis and the greater the value, the higher the peak.
2.2 Pre-processing of Training data
Before getting into details of the explainable variables, I pre-process data by removing all of the missing values. Firstly, I evaluated all the missing values in the dataset by creating a summary of the total number and percentage of missing values of each variable and put it in descending order.
##Evaluate the missing data
# Calculate total count of missing values per column
train_data_transformed<-train_data
total <- colSums(is.na(train_data_transformed))
total <- total[order(-total)]
# Calculate percentage of missing values per column
percent <- colSums(is.na(train_data_transformed)) / nrow(train_data_transformed)
percent <- percent[order(-percent)]
# Create a dataframe with total count and percent of missing values
missing_data <- data.frame(Total = total, Percent = percent)
# Display the first 20 rows
head(missing_data, 20)The PoolQC, MiscFeature, Alley, Fence, FireplaceQu, Lot Frontage are the variables with the most null values, with more than 15%. These factors seem to be not so important in the house buying decision, thus these variables could be removed.
The variables related to Garage including GarageType, GarageYrBlt, GarageFinish, GarageQual and GarageCond have similar percentage of null values. In case of different Basement variables such as BsmtExposure, Bsmt FinType2, BsmtQual, BsmtCond, Bsmt FinType1, they also have quite similar numbers of null values. The reason could be because these variables shows a repetitive in response, therefore we could remove these repeated variables. The variables of Masonry veneer such as MasVnrType and MasVnrArea are non-essential variables and thus also removed completely. Meanwhile, the Electric variable only has one null value, I fill such null value with the mean of the Electric variable.
train_data_transformed <- train_data_transformed[, !(colnames(train_data_transformed) %in% rownames(missing_data)[missing_data$Total > 1])]
library(dplyr)
# Function to calculate the mode
get_mode <- function(x) {
ux <- unique(x)
ux[which.max(tabulate(match(x, ux)))]
}
# Replace missing values with mode
train_data_transformed$Electrical <- ifelse(is.na(train_data_transformed$Electrical),
get_mode(train_data_transformed$Electrical),
train_data_transformed$Electrical)
#Checking if there is still any null value left
max_col_missing <- max(colSums(is.na(train_data_transformed)))
print(max_col_missing)## [1] 0After the removal process, I checked again if there is any missing values available.
2.3 Categorical variables
Then I investigated further into the other 80 explainable variables available. And after understanding the meaning and context of different variables, I make a summary list of features grouping them based on their characteristics and give a preliminary expectation of relation between the explainable variables to the target one in a separate spreadsheet.
Based on my expectations, I selected the most potential features that might have strong relationship with Sale Price, or in other words, the features that are more likely to affect our buying decision of a house. Thus, I ended up with 2 categorical variables and 5 numerical variables among 80 explainable variables.
For the categorical variables, I factorized them into numerical variables to prepare the data for later study.
library(dplyr)
# Define a function to encode categorical variables
encode_categorical <- function(x) {
if (is.factor(x) || is.character(x)) {
levels <- unique(x)
numeric_values <- seq_along(levels)
return(numeric_values[match(x, levels)])
} else {
return(x)
}
}
# Apply the encoding function to the dataframe
train_data_transformed <- train_data_transformed %>% mutate(across(everything(), encode_categorical))
# Print the encoded dataframe
print(train_data_transformed)## # A tibble: 1,460 × 63
## Id MSSubClass MSZoning LotArea Street LotShape LandContour Utilities
## <dbl> <dbl> <int> <dbl> <int> <int> <int> <int>
## 1 1 60 1 8450 1 1 1 1
## 2 2 20 1 9600 1 1 1 1
## 3 3 60 1 11250 1 2 1 1
## 4 4 70 1 9550 1 2 1 1
## 5 5 60 1 14260 1 2 1 1
## 6 6 50 1 14115 1 2 1 1
## 7 7 20 1 10084 1 1 1 1
## 8 8 60 1 10382 1 2 1 1
## 9 9 50 2 6120 1 1 1 1
## 10 10 190 1 7420 1 1 1 1
## # ℹ 1,450 more rows
## # ℹ 55 more variables: LotConfig <int>, LandSlope <int>, Neighborhood <int>,
## # Condition1 <int>, Condition2 <int>, BldgType <int>, HouseStyle <int>,
## # OverallQual <dbl>, OverallCond <dbl>, YearBuilt <dbl>, YearRemodAdd <dbl>,
## # RoofStyle <int>, RoofMatl <int>, Exterior1st <int>, Exterior2nd <int>,
## # ExterQual <int>, ExterCond <int>, Foundation <int>, BsmtFinSF1 <dbl>,
## # BsmtFinSF2 <dbl>, BsmtUnfSF <dbl>, TotalBsmtSF <dbl>, Heating <int>, …glimpse(train_data_transformed)## Rows: 1,460
## Columns: 63
## $ Id <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1…
## $ MSSubClass <dbl> 60, 20, 60, 70, 60, 50, 20, 60, 50, 190, 20, 60, 20, 20,…
## $ MSZoning <int> 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1,…
## $ LotArea <dbl> 8450, 9600, 11250, 9550, 14260, 14115, 10084, 10382, 612…
## $ Street <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ LotShape <int> 1, 1, 2, 2, 2, 2, 1, 2, 1, 1, 1, 2, 3, 2, 2, 1, 2, 1, 1,…
## $ LandContour <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ Utilities <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ LotConfig <int> 1, 2, 1, 3, 2, 1, 1, 3, 1, 3, 1, 1, 1, 1, 3, 3, 4, 1, 1,…
## $ LandSlope <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ Neighborhood <int> 1, 2, 1, 3, 4, 5, 6, 7, 8, 9, 10, 11, 10, 1, 12, 9, 12, …
## $ Condition1 <int> 1, 2, 1, 1, 1, 1, 1, 3, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 5,…
## $ Condition2 <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ BldgType <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 3, 1,…
## $ HouseStyle <int> 1, 2, 1, 1, 1, 3, 2, 1, 3, 4, 2, 1, 2, 2, 2, 4, 2, 2, 2,…
## $ OverallQual <dbl> 7, 6, 7, 7, 8, 5, 8, 7, 7, 5, 5, 9, 5, 7, 6, 7, 6, 4, 5,…
## $ OverallCond <dbl> 5, 8, 5, 5, 5, 5, 5, 6, 5, 6, 5, 5, 6, 5, 5, 8, 7, 5, 5,…
## $ YearBuilt <dbl> 2003, 1976, 2001, 1915, 2000, 1993, 2004, 1973, 1931, 19…
## $ YearRemodAdd <dbl> 2003, 1976, 2002, 1970, 2000, 1995, 2005, 1973, 1950, 19…
## $ RoofStyle <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 1, 2, 1, 1, 1, 1,…
## $ RoofMatl <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ Exterior1st <int> 1, 2, 1, 3, 1, 1, 1, 4, 5, 2, 4, 6, 4, 1, 2, 3, 3, 2, 1,…
## $ Exterior2nd <int> 1, 2, 1, 3, 1, 1, 1, 4, 3, 2, 4, 3, 5, 1, 2, 6, 6, 2, 1,…
## $ ExterQual <int> 1, 2, 1, 2, 1, 2, 1, 2, 2, 2, 2, 3, 2, 1, 2, 2, 2, 2, 2,…
## $ ExterCond <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ Foundation <int> 1, 2, 1, 3, 1, 4, 1, 2, 3, 3, 2, 1, 2, 1, 2, 3, 2, 5, 1,…
## $ BsmtFinSF1 <dbl> 706, 978, 486, 216, 655, 732, 1369, 859, 0, 851, 906, 99…
## $ BsmtFinSF2 <dbl> 0, 0, 0, 0, 0, 0, 0, 32, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ BsmtUnfSF <dbl> 150, 284, 434, 540, 490, 64, 317, 216, 952, 140, 134, 17…
## $ TotalBsmtSF <dbl> 856, 1262, 920, 756, 1145, 796, 1686, 1107, 952, 991, 10…
## $ Heating <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ HeatingQC <int> 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1, 3, 1, 3, 1, 1, 3, 1,…
## $ CentralAir <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ Electrical <int> 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1,…
## $ `1stFlrSF` <dbl> 856, 1262, 920, 961, 1145, 796, 1694, 1107, 1022, 1077, …
## $ `2ndFlrSF` <dbl> 854, 0, 866, 756, 1053, 566, 0, 983, 752, 0, 0, 1142, 0,…
## $ LowQualFinSF <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ GrLivArea <dbl> 1710, 1262, 1786, 1717, 2198, 1362, 1694, 2090, 1774, 10…
## $ BsmtFullBath <dbl> 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1,…
## $ BsmtHalfBath <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ FullBath <dbl> 2, 2, 2, 1, 2, 1, 2, 2, 2, 1, 1, 3, 1, 2, 1, 1, 1, 2, 1,…
## $ HalfBath <dbl> 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,…
## $ BedroomAbvGr <dbl> 3, 3, 3, 3, 4, 1, 3, 3, 2, 2, 3, 4, 2, 3, 2, 2, 2, 2, 3,…
## $ KitchenAbvGr <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1,…
## $ KitchenQual <int> 1, 2, 1, 1, 1, 2, 1, 2, 2, 2, 2, 3, 2, 1, 2, 2, 2, 2, 1,…
## $ TotRmsAbvGrd <dbl> 8, 6, 6, 7, 9, 5, 7, 7, 8, 5, 5, 11, 4, 7, 5, 5, 5, 6, 6…
## $ Functional <int> 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ Fireplaces <dbl> 0, 1, 1, 1, 1, 0, 1, 2, 2, 2, 0, 2, 0, 1, 1, 0, 1, 0, 0,…
## $ GarageCars <dbl> 2, 2, 2, 3, 3, 2, 2, 2, 2, 1, 1, 3, 1, 3, 1, 2, 2, 2, 2,…
## $ GarageArea <dbl> 548, 460, 608, 642, 836, 480, 636, 484, 468, 205, 384, 7…
## $ PavedDrive <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ WoodDeckSF <dbl> 0, 298, 0, 0, 192, 40, 255, 235, 90, 0, 0, 147, 140, 160…
## $ OpenPorchSF <dbl> 61, 0, 42, 35, 84, 30, 57, 204, 0, 4, 0, 21, 0, 33, 213,…
## $ EnclosedPorch <dbl> 0, 0, 0, 272, 0, 0, 0, 228, 205, 0, 0, 0, 0, 0, 176, 0, …
## $ `3SsnPorch` <dbl> 0, 0, 0, 0, 0, 320, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
## $ ScreenPorch <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 176, 0, 0, 0, 0, 0, …
## $ PoolArea <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ MiscVal <dbl> 0, 0, 0, 0, 0, 700, 0, 350, 0, 0, 0, 0, 0, 0, 0, 0, 700,…
## $ MoSold <dbl> 2, 5, 9, 2, 12, 10, 8, 11, 4, 1, 2, 7, 9, 8, 5, 7, 3, 10…
## $ YrSold <dbl> 2008, 2007, 2008, 2006, 2008, 2009, 2007, 2009, 2008, 20…
## $ SaleType <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1,…
## $ SaleCondition <int> 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 3, 1, 3, 1, 1, 1, 1, 1,…
## $ SalePrice <dbl> 208500, 181500, 223500, 140000, 250000, 143000, 307000, …After that, based on the previous variable investigation and business expectation, I studied the 2 most potential categorical variables and visualize their distribution through the bar graph and use box plots to visualize the relationship between such variable to the target variable, SalePrice.
p_1<- ggplot(data= train_data) + aes(x=OverallQual) + geom_bar()
fig_1 <- ggplotly(p_1)
fig_1ggplot(data = train_data) + aes(x = factor(OverallQual), y = SalePrice) + geom_boxplot()
p_2<- ggplot(data= train_data) + aes(x=YearBuilt) + geom_bar()
fig_2 <- ggplotly(p_2)
fig_2ggplot(data = train_data) + aes(x = factor(YearBuilt), y = SalePrice) + geom_boxplot()
For the Overall Quality variable, the bar graph shows the most values counted around the value 5 and 6. There’s only few count that can get really high quality of 10. And the box plot shows that there’s a clear positive relationship between Sale price and Overall Quality. The higher the Overall Quality, the higher the Sale Price.
For the Year Built feature, the bar graph shows that there are more houses built in the 2000s. And there’s a slight relationship between YearBuilt and Sale price. eventhough it’s not as clear as the correlation between Sale Price and Overall Quality. But we can see from the box plot, the newer the house, the higher the Sale Price.
2.4 Numerical variables
From the prior investigation and expectation study, the 5 most potential numerical variables includes GrLivArea as Above ground living area,TotalBsmtSF as Total basement area, LotArea as Lot size, TotRmsAbvGrd as Total rooms above grad, GarageArea as Size of garage. I also visualize their distribution on Histogram and establish the scatter plots to compare the relationship between these explainable variables and the target variable Sale Price.
p_3<- ggplot(data= train_data) + aes(x=GrLivArea) + geom_histogram()
fig_3 <- ggplotly(p_3)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.fig_3ggplot(data = train_data) +
aes(x = GrLivArea, y = SalePrice) +
geom_jitter()+ geom_smooth(method="gam", formula = y ~s(x)) + theme(axis.text.x = element_text(angle = 45)) 
p_4<- ggplot(data= train_data) + aes(x=TotalBsmtSF) + geom_histogram()
fig_4 <- ggplotly(p_4)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.fig_4ggplot(data = train_data) +
aes(x = TotalBsmtSF, y = SalePrice) +
geom_jitter()+ geom_smooth(method="gam", formula = y ~s(x))
p_5<- ggplot(data= train_data) + aes(x=LotArea) + geom_histogram()
fig_5 <- ggplotly(p_5)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.fig_5ggplot(data = train_data) +
aes(x = LotArea, y = SalePrice) +
geom_jitter()+ geom_smooth(method="gam", formula = y ~s(x)) + theme(axis.text.x = element_text(angle = 45))
p_6<- ggplot(data= train_data) + aes(x=TotRmsAbvGrd) + geom_bar()
fig_6 <- ggplotly(p_6)
fig_6ggplot(data = train_data) +
aes(x = TotRmsAbvGrd, y = SalePrice) +
geom_jitter()+ geom_smooth(method="gam", formula = y ~s(x)) + theme(axis.text.x = element_text(angle = 45))
p_7<- ggplot(data= train_data) + aes(x=GarageArea) + geom_histogram()
fig_7 <- ggplotly(p_7)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.fig_7ggplot(data = train_data) +
aes(x = GarageArea, y = SalePrice) +
geom_jitter()+ geom_smooth(method="gam", formula = y ~s(x)) + theme(axis.text.x = element_text(angle = 45))
From the scatterplots, it’s clear that Sale price has a linear relationship with both GrLivArea and TotalBsmtSF variable. The more the above ground living area, the higher the Sale price. The same thing applied for the Total Basement Square Feet, the larger the basement area leads to the higher the sale price. The other expected variables show only a slight linear relationship with Sale Price.
2.5 Heatmap of correlation
After the quick data examination above, we study further into detail through the use of heatmap to understand the correlation among variables more deeply.
corrmat <- cor(train_data_transformed)
heatmap(corrmat, col = colorRampPalette(c("blue", "white", "red"))(100))
In order to see more clearly the most potential variables that are correlated with sale price. I established the zoomed heatmap with only 10 explainable variables with highest correlation to Sale Price.
n_var<- 10 # number of variables for heatmap
imp_var <- names(head(sort(cor(train_data_transformed)[,"SalePrice"], decreasing = TRUE),n_var))
cor_imp_var <- cor(train_data_transformed[, imp_var])
library(ggplot2)
library(reshape2)
# Reshape correlation matrix to long format
SalePrice_colormap <- melt(cor_imp_var)
SalePrice_colormap$Var1<- imp_var[SalePrice_colormap$Var1]
SalePrice_colormap$Var2<- imp_var[SalePrice_colormap$Var2]
# Create Colormap using ggplot2
ggplot(SalePrice_colormap, aes(x = Var1, y = Var2, fill = value)) +
geom_tile() +
scale_fill_gradient(low = "white", high = "blue") +
geom_text(aes(label = round(value, 2)), color = "black", size = 3) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(x = "", y = "") +
ggtitle("SalePrice Correlation Heatmap") +
theme(plot.title = element_text(hjust = 0.5)) +
coord_fixed() 
From the heatmap above, we can see clearly the 10 variables with highest correlation to Sale price as in the order as followed:
- Overall Qual
- GrLivArea
- GarageCars and Garage Area (these 2 variables are quite similar)
- TotalBsmtSF
- 1st FlrSF
- Full Bath
- TotRmsAbvGrd (similar to GrLivArea variable)
- YearBuilt
The result shows quite similar to what my first expectation of the important house features that have high influence to the Sale Price.
Now, we move to the main part of predicting the Sale prices
3. Data modelling
3.1 Pre-processing of Test data
First, I upload the test dataset and simply check the data as performed earlier.
test_data <- read_csv("test.csv")## Rows: 1459 Columns: 80
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (43): MSZoning, Street, Alley, LotShape, LandContour, Utilities, LotConf...
## dbl (37): Id, MSSubClass, LotFrontage, LotArea, OverallQual, OverallCond, Ye...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.head(test_data)summary(test_data)## Id MSSubClass MSZoning LotFrontage
## Min. :1461 Min. : 20.00 Length:1459 Min. : 21.00
## 1st Qu.:1826 1st Qu.: 20.00 Class :character 1st Qu.: 58.00
## Median :2190 Median : 50.00 Mode :character Median : 67.00
## Mean :2190 Mean : 57.38 Mean : 68.58
## 3rd Qu.:2554 3rd Qu.: 70.00 3rd Qu.: 80.00
## Max. :2919 Max. :190.00 Max. :200.00
## NA's :227
## LotArea Street Alley LotShape
## Min. : 1470 Length:1459 Length:1459 Length:1459
## 1st Qu.: 7391 Class :character Class :character Class :character
## Median : 9399 Mode :character Mode :character Mode :character
## Mean : 9819
## 3rd Qu.:11518
## Max. :56600
##
## LandContour Utilities LotConfig LandSlope
## Length:1459 Length:1459 Length:1459 Length:1459
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Neighborhood Condition1 Condition2 BldgType
## Length:1459 Length:1459 Length:1459 Length:1459
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## HouseStyle OverallQual OverallCond YearBuilt
## Length:1459 Min. : 1.000 Min. :1.000 Min. :1879
## Class :character 1st Qu.: 5.000 1st Qu.:5.000 1st Qu.:1953
## Mode :character Median : 6.000 Median :5.000 Median :1973
## Mean : 6.079 Mean :5.554 Mean :1971
## 3rd Qu.: 7.000 3rd Qu.:6.000 3rd Qu.:2001
## Max. :10.000 Max. :9.000 Max. :2010
##
## YearRemodAdd RoofStyle RoofMatl Exterior1st
## Min. :1950 Length:1459 Length:1459 Length:1459
## 1st Qu.:1963 Class :character Class :character Class :character
## Median :1992 Mode :character Mode :character Mode :character
## Mean :1984
## 3rd Qu.:2004
## Max. :2010
##
## Exterior2nd MasVnrType MasVnrArea ExterQual
## Length:1459 Length:1459 Min. : 0.0 Length:1459
## Class :character Class :character 1st Qu.: 0.0 Class :character
## Mode :character Mode :character Median : 0.0 Mode :character
## Mean : 100.7
## 3rd Qu.: 164.0
## Max. :1290.0
## NA's :15
## ExterCond Foundation BsmtQual BsmtCond
## Length:1459 Length:1459 Length:1459 Length:1459
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## BsmtExposure BsmtFinType1 BsmtFinSF1 BsmtFinType2
## Length:1459 Length:1459 Min. : 0.0 Length:1459
## Class :character Class :character 1st Qu.: 0.0 Class :character
## Mode :character Mode :character Median : 350.5 Mode :character
## Mean : 439.2
## 3rd Qu.: 753.5
## Max. :4010.0
## NA's :1
## BsmtFinSF2 BsmtUnfSF TotalBsmtSF Heating
## Min. : 0.00 Min. : 0.0 Min. : 0 Length:1459
## 1st Qu.: 0.00 1st Qu.: 219.2 1st Qu.: 784 Class :character
## Median : 0.00 Median : 460.0 Median : 988 Mode :character
## Mean : 52.62 Mean : 554.3 Mean :1046
## 3rd Qu.: 0.00 3rd Qu.: 797.8 3rd Qu.:1305
## Max. :1526.00 Max. :2140.0 Max. :5095
## NA's :1 NA's :1 NA's :1
## HeatingQC CentralAir Electrical 1stFlrSF
## Length:1459 Length:1459 Length:1459 Min. : 407.0
## Class :character Class :character Class :character 1st Qu.: 873.5
## Mode :character Mode :character Mode :character Median :1079.0
## Mean :1156.5
## 3rd Qu.:1382.5
## Max. :5095.0
##
## 2ndFlrSF LowQualFinSF GrLivArea BsmtFullBath
## Min. : 0 Min. : 0.000 Min. : 407 Min. :0.0000
## 1st Qu.: 0 1st Qu.: 0.000 1st Qu.:1118 1st Qu.:0.0000
## Median : 0 Median : 0.000 Median :1432 Median :0.0000
## Mean : 326 Mean : 3.543 Mean :1486 Mean :0.4345
## 3rd Qu.: 676 3rd Qu.: 0.000 3rd Qu.:1721 3rd Qu.:1.0000
## Max. :1862 Max. :1064.000 Max. :5095 Max. :3.0000
## NA's :2
## BsmtHalfBath FullBath HalfBath BedroomAbvGr
## Min. :0.0000 Min. :0.000 Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:1.000 1st Qu.:0.0000 1st Qu.:2.000
## Median :0.0000 Median :2.000 Median :0.0000 Median :3.000
## Mean :0.0652 Mean :1.571 Mean :0.3777 Mean :2.854
## 3rd Qu.:0.0000 3rd Qu.:2.000 3rd Qu.:1.0000 3rd Qu.:3.000
## Max. :2.0000 Max. :4.000 Max. :2.0000 Max. :6.000
## NA's :2
## KitchenAbvGr KitchenQual TotRmsAbvGrd Functional
## Min. :0.000 Length:1459 Min. : 3.000 Length:1459
## 1st Qu.:1.000 Class :character 1st Qu.: 5.000 Class :character
## Median :1.000 Mode :character Median : 6.000 Mode :character
## Mean :1.042 Mean : 6.385
## 3rd Qu.:1.000 3rd Qu.: 7.000
## Max. :2.000 Max. :15.000
##
## Fireplaces FireplaceQu GarageType GarageYrBlt
## Min. :0.0000 Length:1459 Length:1459 Min. :1895
## 1st Qu.:0.0000 Class :character Class :character 1st Qu.:1959
## Median :0.0000 Mode :character Mode :character Median :1979
## Mean :0.5812 Mean :1978
## 3rd Qu.:1.0000 3rd Qu.:2002
## Max. :4.0000 Max. :2207
## NA's :78
## GarageFinish GarageCars GarageArea GarageQual
## Length:1459 Min. :0.000 Min. : 0.0 Length:1459
## Class :character 1st Qu.:1.000 1st Qu.: 318.0 Class :character
## Mode :character Median :2.000 Median : 480.0 Mode :character
## Mean :1.766 Mean : 472.8
## 3rd Qu.:2.000 3rd Qu.: 576.0
## Max. :5.000 Max. :1488.0
## NA's :1 NA's :1
## GarageCond PavedDrive WoodDeckSF OpenPorchSF
## Length:1459 Length:1459 Min. : 0.00 Min. : 0.00
## Class :character Class :character 1st Qu.: 0.00 1st Qu.: 0.00
## Mode :character Mode :character Median : 0.00 Median : 28.00
## Mean : 93.17 Mean : 48.31
## 3rd Qu.: 168.00 3rd Qu.: 72.00
## Max. :1424.00 Max. :742.00
##
## EnclosedPorch 3SsnPorch ScreenPorch PoolArea
## Min. : 0.00 Min. : 0.000 Min. : 0.00 Min. : 0.000
## 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.00 1st Qu.: 0.000
## Median : 0.00 Median : 0.000 Median : 0.00 Median : 0.000
## Mean : 24.24 Mean : 1.794 Mean : 17.06 Mean : 1.744
## 3rd Qu.: 0.00 3rd Qu.: 0.000 3rd Qu.: 0.00 3rd Qu.: 0.000
## Max. :1012.00 Max. :360.000 Max. :576.00 Max. :800.000
##
## PoolQC Fence MiscFeature MiscVal
## Length:1459 Length:1459 Length:1459 Min. : 0.00
## Class :character Class :character Class :character 1st Qu.: 0.00
## Mode :character Mode :character Mode :character Median : 0.00
## Mean : 58.17
## 3rd Qu.: 0.00
## Max. :17000.00
##
## MoSold YrSold SaleType SaleCondition
## Min. : 1.000 Min. :2006 Length:1459 Length:1459
## 1st Qu.: 4.000 1st Qu.:2007 Class :character Class :character
## Median : 6.000 Median :2008 Mode :character Mode :character
## Mean : 6.104 Mean :2008
## 3rd Qu.: 8.000 3rd Qu.:2009
## Max. :12.000 Max. :2010
## glimpse(test_data)## Rows: 1,459
## Columns: 80
## $ Id <dbl> 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 14…
## $ MSSubClass <dbl> 20, 20, 60, 60, 120, 60, 20, 60, 20, 20, 120, 160, 160, …
## $ MSZoning <chr> "RH", "RL", "RL", "RL", "RL", "RL", "RL", "RL", "RL", "R…
## $ LotFrontage <dbl> 80, 81, 74, 78, 43, 75, NA, 63, 85, 70, 26, 21, 21, 24, …
## $ LotArea <dbl> 11622, 14267, 13830, 9978, 5005, 10000, 7980, 8402, 1017…
## $ Street <chr> "Pave", "Pave", "Pave", "Pave", "Pave", "Pave", "Pave", …
## $ Alley <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ LotShape <chr> "Reg", "IR1", "IR1", "IR1", "IR1", "IR1", "IR1", "IR1", …
## $ LandContour <chr> "Lvl", "Lvl", "Lvl", "Lvl", "HLS", "Lvl", "Lvl", "Lvl", …
## $ Utilities <chr> "AllPub", "AllPub", "AllPub", "AllPub", "AllPub", "AllPu…
## $ LotConfig <chr> "Inside", "Corner", "Inside", "Inside", "Inside", "Corne…
## $ LandSlope <chr> "Gtl", "Gtl", "Gtl", "Gtl", "Gtl", "Gtl", "Gtl", "Gtl", …
## $ Neighborhood <chr> "NAmes", "NAmes", "Gilbert", "Gilbert", "StoneBr", "Gilb…
## $ Condition1 <chr> "Feedr", "Norm", "Norm", "Norm", "Norm", "Norm", "Norm",…
## $ Condition2 <chr> "Norm", "Norm", "Norm", "Norm", "Norm", "Norm", "Norm", …
## $ BldgType <chr> "1Fam", "1Fam", "1Fam", "1Fam", "TwnhsE", "1Fam", "1Fam"…
## $ HouseStyle <chr> "1Story", "1Story", "2Story", "2Story", "1Story", "2Stor…
## $ OverallQual <dbl> 5, 6, 5, 6, 8, 6, 6, 6, 7, 4, 7, 6, 5, 6, 7, 9, 8, 9, 8,…
## $ OverallCond <dbl> 6, 6, 5, 6, 5, 5, 7, 5, 5, 5, 5, 5, 5, 6, 6, 5, 5, 5, 5,…
## $ YearBuilt <dbl> 1961, 1958, 1997, 1998, 1992, 1993, 1992, 1998, 1990, 19…
## $ YearRemodAdd <dbl> 1961, 1958, 1998, 1998, 1992, 1994, 2007, 1998, 1990, 19…
## $ RoofStyle <chr> "Gable", "Hip", "Gable", "Gable", "Gable", "Gable", "Gab…
## $ RoofMatl <chr> "CompShg", "CompShg", "CompShg", "CompShg", "CompShg", "…
## $ Exterior1st <chr> "VinylSd", "Wd Sdng", "VinylSd", "VinylSd", "HdBoard", "…
## $ Exterior2nd <chr> "VinylSd", "Wd Sdng", "VinylSd", "VinylSd", "HdBoard", "…
## $ MasVnrType <chr> "None", "BrkFace", "None", "BrkFace", "None", "None", "N…
## $ MasVnrArea <dbl> 0, 108, 0, 20, 0, 0, 0, 0, 0, 0, 0, 504, 492, 0, 0, 162,…
## $ ExterQual <chr> "TA", "TA", "TA", "TA", "Gd", "TA", "TA", "TA", "TA", "T…
## $ ExterCond <chr> "TA", "TA", "TA", "TA", "TA", "TA", "Gd", "TA", "TA", "T…
## $ Foundation <chr> "CBlock", "CBlock", "PConc", "PConc", "PConc", "PConc", …
## $ BsmtQual <chr> "TA", "TA", "Gd", "TA", "Gd", "Gd", "Gd", "Gd", "Gd", "T…
## $ BsmtCond <chr> "TA", "TA", "TA", "TA", "TA", "TA", "TA", "TA", "TA", "T…
## $ BsmtExposure <chr> "No", "No", "No", "No", "No", "No", "No", "No", "Gd", "N…
## $ BsmtFinType1 <chr> "Rec", "ALQ", "GLQ", "GLQ", "ALQ", "Unf", "ALQ", "Unf", …
## $ BsmtFinSF1 <dbl> 468, 923, 791, 602, 263, 0, 935, 0, 637, 804, 1051, 156,…
## $ BsmtFinType2 <chr> "LwQ", "Unf", "Unf", "Unf", "Unf", "Unf", "Unf", "Unf", …
## $ BsmtFinSF2 <dbl> 144, 0, 0, 0, 0, 0, 0, 0, 0, 78, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ BsmtUnfSF <dbl> 270, 406, 137, 324, 1017, 763, 233, 789, 663, 0, 354, 32…
## $ TotalBsmtSF <dbl> 882, 1329, 928, 926, 1280, 763, 1168, 789, 1300, 882, 14…
## $ Heating <chr> "GasA", "GasA", "GasA", "GasA", "GasA", "GasA", "GasA", …
## $ HeatingQC <chr> "TA", "TA", "Gd", "Ex", "Ex", "Gd", "Ex", "Gd", "Gd", "T…
## $ CentralAir <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "…
## $ Electrical <chr> "SBrkr", "SBrkr", "SBrkr", "SBrkr", "SBrkr", "SBrkr", "S…
## $ `1stFlrSF` <dbl> 896, 1329, 928, 926, 1280, 763, 1187, 789, 1341, 882, 13…
## $ `2ndFlrSF` <dbl> 0, 0, 701, 678, 0, 892, 0, 676, 0, 0, 0, 504, 567, 601, …
## $ LowQualFinSF <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ GrLivArea <dbl> 896, 1329, 1629, 1604, 1280, 1655, 1187, 1465, 1341, 882…
## $ BsmtFullBath <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ BsmtHalfBath <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ FullBath <dbl> 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 2, 2, 2, 2,…
## $ HalfBath <dbl> 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0,…
## $ BedroomAbvGr <dbl> 2, 3, 3, 3, 2, 3, 3, 3, 2, 2, 2, 2, 3, 3, 2, 3, 3, 3, 3,…
## $ KitchenAbvGr <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ KitchenQual <chr> "TA", "Gd", "TA", "Gd", "Gd", "TA", "TA", "TA", "Gd", "T…
## $ TotRmsAbvGrd <dbl> 5, 6, 6, 7, 5, 7, 6, 7, 5, 4, 5, 5, 6, 6, 4, 10, 7, 7, 8…
## $ Functional <chr> "Typ", "Typ", "Typ", "Typ", "Typ", "Typ", "Typ", "Typ", …
## $ Fireplaces <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1,…
## $ FireplaceQu <chr> NA, NA, "TA", "Gd", NA, "TA", NA, "Gd", "Po", NA, "Fa", …
## $ GarageType <chr> "Attchd", "Attchd", "Attchd", "Attchd", "Attchd", "Attch…
## $ GarageYrBlt <dbl> 1961, 1958, 1997, 1998, 1992, 1993, 1992, 1998, 1990, 19…
## $ GarageFinish <chr> "Unf", "Unf", "Fin", "Fin", "RFn", "Fin", "Fin", "Fin", …
## $ GarageCars <dbl> 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 1, 3, 3, 3, 3,…
## $ GarageArea <dbl> 730, 312, 482, 470, 506, 440, 420, 393, 506, 525, 511, 2…
## $ GarageQual <chr> "TA", "TA", "TA", "TA", "TA", "TA", "TA", "TA", "TA", "T…
## $ GarageCond <chr> "TA", "TA", "TA", "TA", "TA", "TA", "TA", "TA", "TA", "T…
## $ PavedDrive <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "…
## $ WoodDeckSF <dbl> 140, 393, 212, 360, 0, 157, 483, 0, 192, 240, 203, 275, …
## $ OpenPorchSF <dbl> 0, 36, 34, 36, 82, 84, 21, 75, 0, 0, 68, 0, 0, 0, 30, 13…
## $ EnclosedPorch <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3SsnPorch` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ ScreenPorch <dbl> 120, 0, 0, 0, 144, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ PoolArea <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ PoolQC <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ Fence <chr> "MnPrv", NA, "MnPrv", NA, NA, NA, "GdPrv", NA, NA, "MnPr…
## $ MiscFeature <chr> NA, "Gar2", NA, NA, NA, NA, "Shed", NA, NA, NA, NA, NA, …
## $ MiscVal <dbl> 0, 12500, 0, 0, 0, 0, 500, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ MoSold <dbl> 6, 6, 3, 6, 1, 4, 3, 5, 2, 4, 6, 2, 3, 6, 6, 1, 6, 6, 2,…
## $ YrSold <dbl> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 20…
## $ SaleType <chr> "WD", "WD", "WD", "WD", "WD", "WD", "WD", "WD", "WD", "W…
## $ SaleCondition <chr> "Normal", "Normal", "Normal", "Normal", "Normal", "Norma…The test dataset only contains 1459 observations and 80 variables compared to the training dataset as the target variable SalePrice is not available.
Then I pre-process the test data by removing the missing values as before.
##Evaluate the missing data
# Calculate total count of missing values per column
test_data_transformed<-test_data
t_total<- colSums(is.na(test_data_transformed))
t_total<- t_total[order(-t_total)]
# Calculate percentage of missing values per column
t_percent<- colSums(is.na(test_data_transformed)) / nrow(test_data_transformed)
t_percent<- t_percent[order(-t_percent)]
# Create a dataframe with total count and percent of missing values
missing_data_t<- data.frame(Total_test = t_total, Percent_test = t_percent)
# Display the first 20 rows
head(missing_data_t, 40)I also removed completely the variables with more than 15% of missing data and the non-essential ones. For the remaining variables, i filled the remaining missing values with the mean of such variable if they are numerical or return the mode if the variables are categorical and checked again if there is any missing values left.
test_data_transformed <- test_data_transformed[, !(colnames(test_data_transformed) %in% rownames(missing_data_t)[missing_data_t$Total_test > 4])]
# Replace missing values with mode for the categorical variables or mean for the numerical variables
test_data_transformed <- test_data_transformed %>%
mutate(
MSZoning = ifelse(is.na(MSZoning), as.character(get_mode(MSZoning)), MSZoning),
Utilities = ifelse(is.na(Utilities), as.character(get_mode(Utilities)), Utilities),
BsmtFullBath = ifelse(is.na(BsmtFullBath), mean(BsmtFullBath, na.rm = TRUE), BsmtFullBath),
BsmtHalfBath = ifelse(is.na(BsmtHalfBath), mean(BsmtHalfBath, na.rm = TRUE), BsmtHalfBath),
Functional = ifelse(is.na(Functional), as.character(get_mode(Functional)), Functional),
Exterior1st = ifelse(is.na(Exterior1st), as.character(get_mode(Exterior1st)), Exterior1st),
Exterior2nd = ifelse(is.na(Exterior2nd), as.character(get_mode(Exterior2nd)), Exterior2nd),
BsmtFinSF1 = ifelse(is.na(BsmtFinSF1), mean(BsmtFinSF1, na.rm = TRUE), BsmtFinSF1),
BsmtFinSF2 = ifelse(is.na(BsmtFinSF2), mean(BsmtFinSF2, na.rm = TRUE), BsmtFinSF2),
BsmtUnfSF = ifelse(is.na(BsmtUnfSF), mean(BsmtUnfSF, na.rm = TRUE), BsmtUnfSF),
TotalBsmtSF = ifelse(is.na(TotalBsmtSF), mean(TotalBsmtSF, na.rm = TRUE), TotalBsmtSF),
KitchenQual = ifelse(is.na(KitchenQual), as.character(get_mode(KitchenQual)), KitchenQual),
GarageCars = ifelse(is.na(GarageCars), mean(GarageCars, na.rm = TRUE), GarageCars),
GarageArea = ifelse(is.na(GarageArea), mean(GarageArea, na.rm = TRUE), GarageArea),
SaleType = ifelse(is.na(SaleType), as.character(get_mode(SaleType)), SaleType)
)
#Checking if there is still any null value left
max_col_missing_t<- max(colSums(is.na(test_data_transformed)))
print(max_col_missing_t)## [1] 0Then i encoded the categorical variables in test dataset into numerical variables to prepare for data aggregation.
# Apply the encoding function (established earlier) to the dataframe
test_data_transformed <- test_data_transformed %>% mutate(across(everything(), encode_categorical))
# Print the encoded dataframe
print(test_data_transformed)## # A tibble: 1,459 × 62
## Id MSSubClass MSZoning LotArea Street LotShape LandContour Utilities
## <dbl> <dbl> <int> <dbl> <int> <int> <int> <int>
## 1 1461 20 1 11622 1 1 1 1
## 2 1462 20 2 14267 1 2 1 1
## 3 1463 60 2 13830 1 2 1 1
## 4 1464 60 2 9978 1 2 1 1
## 5 1465 120 2 5005 1 2 2 1
## 6 1466 60 2 10000 1 2 1 1
## 7 1467 20 2 7980 1 2 1 1
## 8 1468 60 2 8402 1 2 1 1
## 9 1469 20 2 10176 1 1 1 1
## 10 1470 20 2 8400 1 1 1 1
## # ℹ 1,449 more rows
## # ℹ 54 more variables: LotConfig <int>, LandSlope <int>, Neighborhood <int>,
## # Condition1 <int>, Condition2 <int>, BldgType <int>, HouseStyle <int>,
## # OverallQual <dbl>, OverallCond <dbl>, YearBuilt <dbl>, YearRemodAdd <dbl>,
## # RoofStyle <int>, RoofMatl <int>, Exterior1st <int>, Exterior2nd <int>,
## # ExterQual <int>, ExterCond <int>, Foundation <int>, BsmtFinSF1 <dbl>,
## # BsmtFinSF2 <dbl>, BsmtUnfSF <dbl>, TotalBsmtSF <dbl>, Heating <int>, …glimpse(test_data_transformed)## Rows: 1,459
## Columns: 62
## $ Id <dbl> 1461, 1462, 1463, 1464, 1465, 1466, 1467, 1468, 1469, 14…
## $ MSSubClass <dbl> 20, 20, 60, 60, 120, 60, 20, 60, 20, 20, 120, 160, 160, …
## $ MSZoning <int> 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 3, 3, 2, 2, 2, 2, 2, 2,…
## $ LotArea <dbl> 11622, 14267, 13830, 9978, 5005, 10000, 7980, 8402, 1017…
## $ Street <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ LotShape <int> 1, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 2, 2, 1, 2,…
## $ LandContour <int> 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ Utilities <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ LotConfig <int> 1, 2, 1, 1, 1, 2, 1, 1, 1, 2, 3, 1, 1, 3, 3, 1, 2, 1, 1,…
## $ LandSlope <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ Neighborhood <int> 1, 1, 2, 2, 3, 2, 2, 2, 2, 1, 1, 4, 4, 5, 5, 6, 6, 6, 6,…
## $ Condition1 <int> 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2,…
## $ Condition2 <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ BldgType <int> 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 3, 3, 3, 3, 1, 1, 1, 1,…
## $ HouseStyle <int> 1, 1, 2, 2, 1, 2, 1, 2, 1, 1, 1, 2, 2, 2, 1, 2, 1, 1, 1,…
## $ OverallQual <dbl> 5, 6, 5, 6, 8, 6, 6, 6, 7, 4, 7, 6, 5, 6, 7, 9, 8, 9, 8,…
## $ OverallCond <dbl> 6, 6, 5, 6, 5, 5, 7, 5, 5, 5, 5, 5, 5, 6, 6, 5, 5, 5, 5,…
## $ YearBuilt <dbl> 1961, 1958, 1997, 1998, 1992, 1993, 1992, 1998, 1990, 19…
## $ YearRemodAdd <dbl> 1961, 1958, 1998, 1998, 1992, 1994, 2007, 1998, 1990, 19…
## $ RoofStyle <int> 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,…
## $ RoofMatl <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ Exterior1st <int> 1, 2, 1, 1, 3, 3, 3, 1, 3, 4, 5, 3, 3, 4, 4, 1, 1, 1, 6,…
## $ Exterior2nd <int> 1, 2, 1, 1, 3, 3, 3, 1, 3, 4, 5, 3, 3, 6, 6, 1, 1, 1, 7,…
## $ ExterQual <int> 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 3, 2, 2, 2,…
## $ ExterCond <int> 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ Foundation <int> 1, 1, 2, 2, 2, 2, 2, 2, 2, 1, 2, 1, 1, 1, 1, 2, 2, 2, 2,…
## $ BsmtFinSF1 <dbl> 468, 923, 791, 602, 263, 0, 935, 0, 637, 804, 1051, 156,…
## $ BsmtFinSF2 <dbl> 144, 0, 0, 0, 0, 0, 0, 0, 0, 78, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ BsmtUnfSF <dbl> 270, 406, 137, 324, 1017, 763, 233, 789, 663, 0, 354, 32…
## $ TotalBsmtSF <dbl> 882, 1329, 928, 926, 1280, 763, 1168, 789, 1300, 882, 14…
## $ Heating <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ HeatingQC <int> 1, 1, 2, 3, 3, 2, 3, 2, 2, 1, 3, 1, 1, 1, 3, 3, 3, 3, 3,…
## $ CentralAir <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ Electrical <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ `1stFlrSF` <dbl> 896, 1329, 928, 926, 1280, 763, 1187, 789, 1341, 882, 13…
## $ `2ndFlrSF` <dbl> 0, 0, 701, 678, 0, 892, 0, 676, 0, 0, 0, 504, 567, 601, …
## $ LowQualFinSF <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ GrLivArea <dbl> 896, 1329, 1629, 1604, 1280, 1655, 1187, 1465, 1341, 882…
## $ BsmtFullBath <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ BsmtHalfBath <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ FullBath <dbl> 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 2, 1, 1, 2, 1, 2, 2, 2, 2,…
## $ HalfBath <dbl> 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0,…
## $ BedroomAbvGr <dbl> 2, 3, 3, 3, 2, 3, 3, 3, 2, 2, 2, 2, 3, 3, 2, 3, 3, 3, 3,…
## $ KitchenAbvGr <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ KitchenQual <int> 1, 2, 1, 2, 2, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1, 3, 2, 3, 3,…
## $ TotRmsAbvGrd <dbl> 5, 6, 6, 7, 5, 7, 6, 7, 5, 4, 5, 5, 6, 6, 4, 10, 7, 7, 8…
## $ Functional <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ Fireplaces <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1,…
## $ GarageCars <dbl> 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 1, 3, 3, 3, 3,…
## $ GarageArea <dbl> 730, 312, 482, 470, 506, 440, 420, 393, 506, 525, 511, 2…
## $ PavedDrive <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ WoodDeckSF <dbl> 140, 393, 212, 360, 0, 157, 483, 0, 192, 240, 203, 275, …
## $ OpenPorchSF <dbl> 0, 36, 34, 36, 82, 84, 21, 75, 0, 0, 68, 0, 0, 0, 30, 13…
## $ EnclosedPorch <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ `3SsnPorch` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ ScreenPorch <dbl> 120, 0, 0, 0, 144, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ PoolArea <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ MiscVal <dbl> 0, 12500, 0, 0, 0, 0, 500, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
## $ MoSold <dbl> 6, 6, 3, 6, 1, 4, 3, 5, 2, 4, 6, 2, 3, 6, 6, 1, 6, 6, 2,…
## $ YrSold <dbl> 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 2010, 20…
## $ SaleType <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 3, 3, 1, 1,…
## $ SaleCondition <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1,…3.2 Data aggregation
Before data aggregation, I check if the data types are consistent between the training and test dataset. After confirming the data type consistency, i then proceed to concatenate the 2 datasets and name it all_data.
##Check the data type consistency between the train and test dataset
# Get the column names for data type consistency check
selected_columns <- intersect(colnames(train_data_transformed), colnames(test_data_transformed))
# Get the data types of the corresponding columns in each data frame
train_types <- sapply(train_data_transformed[selected_columns], class)
test_types <- sapply(test_data_transformed[selected_columns], class)
# Check for data type consistency
consistent_data_type <- all(train_types == test_types)
# Print the result
if (consistent_data_type) {
cat("Data types are consistent between the two datasets \n")
} else {
cat("Data types are not consistent between the two datasets \n")
cat("Data types in train dataset:\n")
print(train_types)
cat("Data types in test dataset:\n")
print(test_types)
}## Data types are consistent between the two datasets## Data aggregation
all_data <- rbind(train_data_transformed[,selected_columns],test_data_transformed[,selected_columns])
all_data <- all_data[, !names(all_data) %in% "Id"]
all_data3.3 Data transformation
Afterwards, I performed data transformation for the Sale Price variable to address skewness to avoid misleading result and prepare for data modelling. The log transformation proposes the calculations of the natural logarithm for each value in the dataset. The Sale Price data after log transformation is visualized with the histogram.
library("ggplot2")
# Create prices DataFrame
log_prices <- data.frame(price = train_data_transformed$SalePrice, log_price = log1p(train_data_transformed$SalePrice))
# Generate histogram
#ggplot(log_prices, aes(x = log_price)) + geom_histogram()
p_log <- ggplot(data= log_prices) + aes(x=log_price) + geom_histogram()
fig_p_log <-ggplotly(p_log)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.fig_p_logI also selected the numeric features that are extremely skewed to scale them to make them more normal distributed to fit model requirement.
library(e1071)##
## Attaching package: 'e1071'## The following objects are masked from 'package:moments':
##
## kurtosis, moment, skewness# Log transform the target variable
train_data_transformed$SalePrice <- log1p(train_data_transformed$SalePrice)
# Log transform skewed numeric features
numeric_features <- names(Filter(is.numeric, all_data))
# Compute skewness
skewed_features <- sapply(train_data_transformed[, numeric_features], function(x) skewness(x))
skewed_features <- names(skewed_features[skewed_features > 0.75])
all_data[, skewed_features] <- log1p(all_data[, skewed_features])3.4 Ridge Regression Model
Afterwards, I prepared data by creating the training and test feature matrices for modeling. Then, I defined the function for calculating the Root Mean Squared Error (RMSE) for cross-validation purpose. I then use glmnet function to initialize the ridge regression model using the training feature matrix, y target variable and lambda penalty parameter.
Alpha values are used as tuning parameters for testing and saved in a vector called alphas. Cross-validation is then performed by iterating through different alpha values in the alphas vector. A ridge regression model is fitted for each alpha value, the RMSE is calculated using the defined function and the results are stored in crvalid_ridge.
The cross-validation results are then converted to a dataframe with column for alpha and RMSE. The plot was generated based on such dataframe to show the relationship between alphas and RMSE.
The minimum RMSE and the according optimal alpha value are found by filtering the result in the dataframe above.
The final ridge regression model is fit using the optimal alpha value of 0.05. The model is trained on the complete training data and now ready for predicting sale prices on the test data by using X_test as the input feature matrix
##Data preparation: Creating matrices in R for modeling:
X_train <- all_data[1:nrow(train_data_transformed), ]
X_test <- all_data[(nrow(train_data_transformed) + 1):nrow(all_data), ]
y <- train_data_transformed$SalePrice
## Package and Function loading:
# Load the required packages
library(glmnet)## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following objects are masked from 'package:tidyr':
##
## expand, pack, unpack## Loaded glmnet 4.1-7library(ggplot2)
# RMSE Calculation Function:
rmse_cv <- function(model) {
rmse <- sqrt(mean((predict(model, newx = as.matrix(X_train)) - y)^2))
return(rmse)
}
# Initialize Ridge model
model_ridge <- glmnet(as.matrix(X_train), y, alpha = 0, lambda = 1)
##Cross-validation and Model Selection:
# Define alpha values- tuning parameters for Ridge model
alphas <- c(0.05, 0.1, 0.3, 1, 3, 5, 10, 15, 30, 50, 75)
# Perform cross-validation for different alpha values
crvalid_ridge <- sapply(alphas, function(alpha) {
model <- glmnet(as.matrix(X_train), y, alpha = 0, lambda = alpha)
rmse_cv(model)
})
# Convert the result to a data frame
crvalid_ridge <- data.frame(alpha = alphas, rmse = crvalid_ridge)
# Plot the RMSE values against alpha
ggplot(crvalid_ridge, aes(x = alpha, y = rmse)) +
geom_line() +
labs(title = "Validation", x = "alpha", y = "rmse")
##Model Tuning and Final Model:
# Find the alpha value with the minimum RMSE
min_rmse <- min(crvalid_ridge$rmse)
min_rmse## [1] 0.1298651crvalid_ridge %>% filter(rmse == min_rmse)#Fit the final ridege regrestion model using the optimal alpha value
final_model <- glmnet(as.matrix(X_train), y, alpha = 0, lambda = 0.05)In order to generate predictions for the test data, the final model is used and X_test is passed as the new feature matrix for prediction.
predictions <- predict(final_model, newx = as.matrix(X_test))
head(predictions)## s0
## [1,] 11.80711
## [2,] 12.00854
## [3,] 12.08711
## [4,] 12.16929
## [5,] 12.11982
## [6,] 12.04464# Create a data frame with the test IDs and predicted sale prices
submission <- data.frame(Id = test_data_transformed$Id, Sale_Price = predictions)
# Change the column label to "Sale Price"
colnames(submission)[2] <- "SalePrice"
# Write the submission data frame to a CSV file
write.csv(submission, file = "House_Prices_submission_WinnPham.csv", row.names = FALSE)